As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations is …

Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector
database and many data center applications, such as person re-identification and …

Modern recommendation systems integrate graph convolution neural networks (GCN) for
enhancing embedding representation. Compared with widely deployed neural network …

Today's computing systems require moving data back-and-forth between computing
resources (eg, CPUs, GPUs, accelerators) and off-chip main memory so that computation …

Over the past decade, there has been a persistent trend in edge computing, driving the
migration of intelligence closer to the edge. The increasing need to process data locally has …

ReRAM-based Processing-In-Memory (PIM) architectures have been increasingly explored
to accelerate various Deep Neural Network (DNN) applications because they can achieve …

This paper presents a study of methods for mapping the convolutional workloads in deep
neural networks (DNNs) onto the computing hardware in the in-memory computing (IMC) …

Graph application is ever-growing in relational data analysis. However, the memory access
patterns become the performance bottleneck in graph analytics and graph neural network …

Deep learning-based personalized recommendation models (DLRMs) are dominating AI
tasks in data centers. The performance bottleneck of typical DLRMs mainly lies in the …